Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations9986
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Text6
DateTime2
Categorical7
Numeric6

Alerts

Country has constant value "united states" Constant
Category is highly overall correlated with Sub-CategoryHigh correlation
Discount is highly overall correlated with Profit and 1 other fieldsHigh correlation
Postal Code is highly overall correlated with Region and 1 other fieldsHigh correlation
Profit is highly overall correlated with Discount and 2 other fieldsHigh correlation
Profit Margin is highly overall correlated with Discount and 1 other fieldsHigh correlation
Region is highly overall correlated with Postal Code and 1 other fieldsHigh correlation
Sales is highly overall correlated with ProfitHigh correlation
State is highly overall correlated with Postal Code and 1 other fieldsHigh correlation
Sub-Category is highly overall correlated with CategoryHigh correlation
Discount has 4793 (48.0%) zeros Zeros

Reproduction

Analysis started2025-07-17 08:50:47.692656
Analysis finished2025-07-17 08:50:53.343545
Duration5.65 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct5009
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2025-07-17T14:20:53.624441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters139804
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2540 ?
Unique (%)25.4%

Sample

1st rowCA-2014-100006
2nd rowCA-2014-100090
3rd rowCA-2014-100090
4th rowCA-2014-100293
5th rowCA-2014-100328
ValueCountFrequency (%)
ca-2017-100111 14
 
0.1%
ca-2017-157987 12
 
0.1%
us-2016-108504 11
 
0.1%
ca-2016-165330 11
 
0.1%
us-2015-126977 10
 
0.1%
ca-2016-105732 10
 
0.1%
ca-2015-131338 10
 
0.1%
ca-2014-106439 9
 
0.1%
ca-2015-132626 9
 
0.1%
ca-2015-158421 9
 
0.1%
Other values (4999) 9881
98.9%
2025-07-17T14:20:54.050647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 25486
18.2%
- 19972
14.3%
0 15478
11.1%
2 15369
11.0%
C 8302
 
5.9%
A 8302
 
5.9%
6 7900
 
5.7%
7 7431
 
5.3%
4 7396
 
5.3%
5 7332
 
5.2%
Other values (5) 16836
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99860
71.4%
Dash Punctuation 19972
 
14.3%
Uppercase Letter 19972
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25486
25.5%
0 15478
15.5%
2 15369
15.4%
6 7900
 
7.9%
7 7431
 
7.4%
4 7396
 
7.4%
5 7332
 
7.3%
3 5444
 
5.5%
8 4041
 
4.0%
9 3983
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 8302
41.6%
A 8302
41.6%
U 1684
 
8.4%
S 1684
 
8.4%
Dash Punctuation
ValueCountFrequency (%)
- 19972
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119832
85.7%
Latin 19972
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25486
21.3%
- 19972
16.7%
0 15478
12.9%
2 15369
12.8%
6 7900
 
6.6%
7 7431
 
6.2%
4 7396
 
6.2%
5 7332
 
6.1%
3 5444
 
4.5%
8 4041
 
3.4%
Latin
ValueCountFrequency (%)
C 8302
41.6%
A 8302
41.6%
U 1684
 
8.4%
S 1684
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25486
18.2%
- 19972
14.3%
0 15478
11.1%
2 15369
11.0%
C 8302
 
5.9%
A 8302
 
5.9%
6 7900
 
5.7%
7 7431
 
5.3%
4 7396
 
5.3%
5 7332
 
5.2%
Other values (5) 16836
12.0%
Distinct1862
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2025-07-17T14:20:54.360856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters149790
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st rowTEC-PH-10002075
2nd rowFUR-TA-10003715
3rd rowOFF-BI-10001597
4th rowOFF-PA-10000176
5th rowOFF-BI-10000343
ValueCountFrequency (%)
tec-ac-10003832 18
 
0.2%
off-pa-10001970 18
 
0.2%
fur-fu-10004270 16
 
0.2%
fur-ch-10002647 15
 
0.2%
tec-ac-10003628 15
 
0.2%
fur-ch-10001146 15
 
0.2%
tec-ac-10002049 15
 
0.2%
off-pa-10002377 14
 
0.1%
fur-ch-10003774 14
 
0.1%
off-bi-10001524 14
 
0.1%
Other values (1852) 9832
98.5%
2025-07-17T14:20:54.775965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35022
23.4%
- 19972
13.3%
F 15336
10.2%
1 14985
10.0%
O 6318
 
4.2%
2 4859
 
3.2%
4 4828
 
3.2%
3 4803
 
3.2%
A 4418
 
2.9%
5 3398
 
2.3%
Other values (17) 35851
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79888
53.3%
Uppercase Letter 49930
33.3%
Dash Punctuation 19972
 
13.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 15336
30.7%
O 6318
12.7%
A 4418
 
8.8%
C 3302
 
6.6%
U 3265
 
6.5%
T 3009
 
6.0%
R 2915
 
5.8%
P 2723
 
5.5%
E 2099
 
4.2%
B 1750
 
3.5%
Other values (6) 4795
 
9.6%
Decimal Number
ValueCountFrequency (%)
0 35022
43.8%
1 14985
18.8%
2 4859
 
6.1%
4 4828
 
6.0%
3 4803
 
6.0%
5 3398
 
4.3%
7 3102
 
3.9%
9 3046
 
3.8%
6 2993
 
3.7%
8 2852
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 19972
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99860
66.7%
Latin 49930
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 15336
30.7%
O 6318
12.7%
A 4418
 
8.8%
C 3302
 
6.6%
U 3265
 
6.5%
T 3009
 
6.0%
R 2915
 
5.8%
P 2723
 
5.5%
E 2099
 
4.2%
B 1750
 
3.5%
Other values (6) 4795
 
9.6%
Common
ValueCountFrequency (%)
0 35022
35.1%
- 19972
20.0%
1 14985
15.0%
2 4859
 
4.9%
4 4828
 
4.8%
3 4803
 
4.8%
5 3398
 
3.4%
7 3102
 
3.1%
9 3046
 
3.1%
6 2993
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35022
23.4%
- 19972
13.3%
F 15336
10.2%
1 14985
10.0%
O 6318
 
4.2%
2 4859
 
3.2%
4 4828
 
3.2%
3 4803
 
3.2%
A 4418
 
2.9%
5 3398
 
2.3%
Other values (17) 35851
23.9%
Distinct1237
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Minimum2014-01-03 00:00:00
Maximum2017-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-17T14:20:54.914329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:55.061057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1334
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Minimum2014-01-07 00:00:00
Maximum2018-01-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-17T14:20:55.206763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:55.359810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ship Mode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
standard class
5964 
second class
1942 
first class
1537 
same day
 
543

Length

Max length14
Median length14
Mean length12.823052
Min length8

Characters and Unicode

Total characters128051
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstandard class
2nd rowstandard class
3rd rowstandard class
4th rowstandard class
5th rowstandard class

Common Values

ValueCountFrequency (%)
standard class 5964
59.7%
second class 1942
 
19.4%
first class 1537
 
15.4%
same day 543
 
5.4%

Length

2025-07-17T14:20:55.504723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T14:20:55.610441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
class 9443
47.3%
standard 5964
29.9%
second 1942
 
9.7%
first 1537
 
7.7%
same 543
 
2.7%
day 543
 
2.7%

Most occurring characters

ValueCountFrequency (%)
s 28872
22.5%
a 22457
17.5%
d 14413
11.3%
c 11385
 
8.9%
9986
 
7.8%
l 9443
 
7.4%
n 7906
 
6.2%
t 7501
 
5.9%
r 7501
 
5.9%
e 2485
 
1.9%
Other values (5) 6102
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 118065
92.2%
Space Separator 9986
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 28872
24.5%
a 22457
19.0%
d 14413
12.2%
c 11385
 
9.6%
l 9443
 
8.0%
n 7906
 
6.7%
t 7501
 
6.4%
r 7501
 
6.4%
e 2485
 
2.1%
o 1942
 
1.6%
Other values (4) 4160
 
3.5%
Space Separator
ValueCountFrequency (%)
9986
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118065
92.2%
Common 9986
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 28872
24.5%
a 22457
19.0%
d 14413
12.2%
c 11385
 
9.6%
l 9443
 
8.0%
n 7906
 
6.7%
t 7501
 
6.4%
r 7501
 
6.4%
e 2485
 
2.1%
o 1942
 
1.6%
Other values (4) 4160
 
3.5%
Common
ValueCountFrequency (%)
9986
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 28872
22.5%
a 22457
17.5%
d 14413
11.3%
c 11385
 
8.9%
9986
 
7.8%
l 9443
 
7.4%
n 7906
 
6.2%
t 7501
 
5.9%
r 7501
 
5.9%
e 2485
 
1.9%
Other values (5) 6102
 
4.8%
Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2025-07-17T14:20:56.024330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters69902
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowdk13375
2nd roweb13705
3rd roweb13705
4th rownf18475
5th rowjc15340
ValueCountFrequency (%)
wb21850 37
 
0.4%
pp18955 34
 
0.3%
ma17560 34
 
0.3%
jl15835 34
 
0.3%
jd15895 32
 
0.3%
eh13765 32
 
0.3%
sv20365 32
 
0.3%
ck12205 32
 
0.3%
ap10915 31
 
0.3%
ep13915 31
 
0.3%
Other values (783) 9657
96.7%
2025-07-17T14:20:56.549444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11905
17.0%
0 8524
12.2%
5 7859
 
11.2%
2 4679
 
6.7%
7 2927
 
4.2%
6 2905
 
4.2%
9 2901
 
4.2%
8 2817
 
4.0%
3 2779
 
4.0%
4 2634
 
3.8%
Other values (26) 19972
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49930
71.4%
Lowercase Letter 19972
 
28.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1795
 
9.0%
c 1723
 
8.6%
m 1711
 
8.6%
b 1638
 
8.2%
d 1296
 
6.5%
a 1226
 
6.1%
p 1134
 
5.7%
j 1133
 
5.7%
h 968
 
4.8%
k 932
 
4.7%
Other values (16) 6416
32.1%
Decimal Number
ValueCountFrequency (%)
1 11905
23.8%
0 8524
17.1%
5 7859
15.7%
2 4679
 
9.4%
7 2927
 
5.9%
6 2905
 
5.8%
9 2901
 
5.8%
8 2817
 
5.6%
3 2779
 
5.6%
4 2634
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49930
71.4%
Latin 19972
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1795
 
9.0%
c 1723
 
8.6%
m 1711
 
8.6%
b 1638
 
8.2%
d 1296
 
6.5%
a 1226
 
6.1%
p 1134
 
5.7%
j 1133
 
5.7%
h 968
 
4.8%
k 932
 
4.7%
Other values (16) 6416
32.1%
Common
ValueCountFrequency (%)
1 11905
23.8%
0 8524
17.1%
5 7859
15.7%
2 4679
 
9.4%
7 2927
 
5.9%
6 2905
 
5.8%
9 2901
 
5.8%
8 2817
 
5.6%
3 2779
 
5.6%
4 2634
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11905
17.0%
0 8524
12.2%
5 7859
 
11.2%
2 4679
 
6.7%
7 2927
 
4.2%
6 2905
 
4.2%
9 2901
 
4.2%
8 2817
 
4.0%
3 2779
 
4.0%
4 2634
 
3.8%
Other values (26) 19972
28.6%
Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2025-07-17T14:20:57.005493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.945524
Min length7

Characters and Unicode

Total characters129274
Distinct characters30
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowdennis kane
2nd rowed braxton
3rd rowed braxton
4th rowneil französisch
5th rowjasper cacioppo
ValueCountFrequency (%)
michael 120
 
0.6%
frank 112
 
0.6%
john 107
 
0.5%
patrick 96
 
0.5%
stewart 93
 
0.5%
paul 92
 
0.5%
brian 92
 
0.5%
ken 91
 
0.5%
rick 91
 
0.5%
matt 86
 
0.4%
Other values (901) 19057
95.1%
2025-07-17T14:20:57.576457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13280
 
10.3%
e 12451
 
9.6%
n 10787
 
8.3%
r 10394
 
8.0%
10051
 
7.8%
i 7992
 
6.2%
l 7331
 
5.7%
s 6336
 
4.9%
t 6256
 
4.8%
o 6077
 
4.7%
Other values (20) 38319
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 119223
92.2%
Space Separator 10051
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13280
 
11.1%
e 12451
 
10.4%
n 10787
 
9.0%
r 10394
 
8.7%
i 7992
 
6.7%
l 7331
 
6.1%
s 6336
 
5.3%
t 6256
 
5.2%
o 6077
 
5.1%
h 4860
 
4.1%
Other values (19) 33459
28.1%
Space Separator
ValueCountFrequency (%)
10051
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119223
92.2%
Common 10051
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13280
 
11.1%
e 12451
 
10.4%
n 10787
 
9.0%
r 10394
 
8.7%
i 7992
 
6.7%
l 7331
 
6.1%
s 6336
 
5.3%
t 6256
 
5.2%
o 6077
 
5.1%
h 4860
 
4.1%
Other values (19) 33459
28.1%
Common
ValueCountFrequency (%)
10051
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129185
99.9%
None 89
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13280
 
10.3%
e 12451
 
9.6%
n 10787
 
8.4%
r 10394
 
8.0%
10051
 
7.8%
i 7992
 
6.2%
l 7331
 
5.7%
s 6336
 
4.9%
t 6256
 
4.8%
o 6077
 
4.7%
Other values (17) 38230
29.6%
None
ValueCountFrequency (%)
ö 61
68.5%
ä 23
 
25.8%
ü 5
 
5.6%

Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
consumer
5189 
corporate
3019 
home office
1778 

Length

Max length11
Median length8
Mean length8.8364711
Min length8

Characters and Unicode

Total characters88241
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconsumer
2nd rowcorporate
3rd rowcorporate
4th rowhome office
5th rowconsumer

Common Values

ValueCountFrequency (%)
consumer 5189
52.0%
corporate 3019
30.2%
home office 1778
 
17.8%

Length

2025-07-17T14:20:57.718819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T14:20:57.821298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
consumer 5189
44.1%
corporate 3019
25.7%
home 1778
 
15.1%
office 1778
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o 14783
16.8%
e 11764
13.3%
r 11227
12.7%
c 9986
11.3%
m 6967
7.9%
n 5189
 
5.9%
s 5189
 
5.9%
u 5189
 
5.9%
f 3556
 
4.0%
p 3019
 
3.4%
Other values (5) 11372
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 86463
98.0%
Space Separator 1778
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 14783
17.1%
e 11764
13.6%
r 11227
13.0%
c 9986
11.5%
m 6967
8.1%
n 5189
 
6.0%
s 5189
 
6.0%
u 5189
 
6.0%
f 3556
 
4.1%
p 3019
 
3.5%
Other values (4) 9594
11.1%
Space Separator
ValueCountFrequency (%)
1778
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86463
98.0%
Common 1778
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 14783
17.1%
e 11764
13.6%
r 11227
13.0%
c 9986
11.5%
m 6967
8.1%
n 5189
 
6.0%
s 5189
 
6.0%
u 5189
 
6.0%
f 3556
 
4.1%
p 3019
 
3.5%
Other values (4) 9594
11.1%
Common
ValueCountFrequency (%)
1778
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 14783
16.8%
e 11764
13.3%
r 11227
12.7%
c 9986
11.3%
m 6967
7.9%
n 5189
 
5.9%
s 5189
 
5.9%
u 5189
 
5.9%
f 3556
 
4.0%
p 3019
 
3.4%
Other values (5) 11372
12.9%

Country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
united states
9986 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters129818
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunited states
2nd rowunited states
3rd rowunited states
4th rowunited states
5th rowunited states

Common Values

ValueCountFrequency (%)
united states 9986
100.0%

Length

2025-07-17T14:20:57.939207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T14:20:58.024528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
united 9986
50.0%
states 9986
50.0%

Most occurring characters

ValueCountFrequency (%)
t 29958
23.1%
e 19972
15.4%
s 19972
15.4%
u 9986
 
7.7%
n 9986
 
7.7%
i 9986
 
7.7%
d 9986
 
7.7%
9986
 
7.7%
a 9986
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 119832
92.3%
Space Separator 9986
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 29958
25.0%
e 19972
16.7%
s 19972
16.7%
u 9986
 
8.3%
n 9986
 
8.3%
i 9986
 
8.3%
d 9986
 
8.3%
a 9986
 
8.3%
Space Separator
ValueCountFrequency (%)
9986
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119832
92.3%
Common 9986
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 29958
25.0%
e 19972
16.7%
s 19972
16.7%
u 9986
 
8.3%
n 9986
 
8.3%
i 9986
 
8.3%
d 9986
 
8.3%
a 9986
 
8.3%
Common
ValueCountFrequency (%)
9986
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 29958
23.1%
e 19972
15.4%
s 19972
15.4%
u 9986
 
7.7%
n 9986
 
7.7%
i 9986
 
7.7%
d 9986
 
7.7%
9986
 
7.7%
a 9986
 
7.7%

City
Text

Distinct531
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2025-07-17T14:20:58.521349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.3308632
Min length4

Characters and Unicode

Total characters93178
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.7%

Sample

1st rownew york city
2nd rowsan francisco
3rd rowsan francisco
4th rowjacksonville
5th rownew york city
ValueCountFrequency (%)
city 993
 
7.0%
new 936
 
6.6%
york 919
 
6.5%
san 805
 
5.7%
los 747
 
5.3%
angeles 747
 
5.3%
philadelphia 537
 
3.8%
francisco 510
 
3.6%
seattle 428
 
3.0%
houston 377
 
2.7%
Other values (555) 7227
50.8%
2025-07-17T14:20:59.374799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8829
 
9.5%
e 8828
 
9.5%
o 7679
 
8.2%
n 7327
 
7.9%
l 7275
 
7.8%
s 6434
 
6.9%
i 6273
 
6.7%
r 4850
 
5.2%
t 4691
 
5.0%
c 4474
 
4.8%
Other values (17) 26518
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88938
95.4%
Space Separator 4240
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8829
 
9.9%
e 8828
 
9.9%
o 7679
 
8.6%
n 7327
 
8.2%
l 7275
 
8.2%
s 6434
 
7.2%
i 6273
 
7.1%
r 4850
 
5.5%
t 4691
 
5.3%
c 4474
 
5.0%
Other values (16) 22278
25.0%
Space Separator
ValueCountFrequency (%)
4240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88938
95.4%
Common 4240
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8829
 
9.9%
e 8828
 
9.9%
o 7679
 
8.6%
n 7327
 
8.2%
l 7275
 
8.2%
s 6434
 
7.2%
i 6273
 
7.1%
r 4850
 
5.5%
t 4691
 
5.3%
c 4474
 
5.0%
Other values (16) 22278
25.0%
Common
ValueCountFrequency (%)
4240
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8829
 
9.5%
e 8828
 
9.5%
o 7679
 
8.2%
n 7327
 
7.9%
l 7275
 
7.8%
s 6434
 
6.9%
i 6273
 
6.7%
r 4850
 
5.2%
t 4691
 
5.0%
c 4474
 
4.8%
Other values (17) 26518
28.5%

State
Categorical

High correlation 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
california
2001 
new york
1127 
texas
985 
pennsylvania
587 
washington
506 
Other values (44)
4780 

Length

Max length20
Median length14
Mean length8.4870819
Min length4

Characters and Unicode

Total characters84752
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rownew york
2nd rowcalifornia
3rd rowcalifornia
4th rowflorida
5th rownew york

Common Values

ValueCountFrequency (%)
california 2001
20.0%
new york 1127
 
11.3%
texas 985
 
9.9%
pennsylvania 587
 
5.9%
washington 506
 
5.1%
illinois 492
 
4.9%
ohio 468
 
4.7%
florida 383
 
3.8%
michigan 255
 
2.6%
north carolina 248
 
2.5%
Other values (39) 2934
29.4%

Length

2025-07-17T14:20:59.517488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 2001
17.1%
new 1321
 
11.3%
york 1127
 
9.6%
texas 985
 
8.4%
pennsylvania 587
 
5.0%
washington 506
 
4.3%
illinois 492
 
4.2%
ohio 468
 
4.0%
florida 383
 
3.3%
carolina 290
 
2.5%
Other values (43) 3536
30.2%

Most occurring characters

ValueCountFrequency (%)
a 11097
13.1%
i 10634
12.5%
n 9739
11.5%
o 7974
9.4%
r 5594
 
6.6%
e 5049
 
6.0%
l 4861
 
5.7%
s 4654
 
5.5%
c 3413
 
4.0%
t 2766
 
3.3%
Other values (16) 18971
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83042
98.0%
Space Separator 1710
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11097
13.4%
i 10634
12.8%
n 9739
11.7%
o 7974
9.6%
r 5594
 
6.7%
e 5049
 
6.1%
l 4861
 
5.9%
s 4654
 
5.6%
c 3413
 
4.1%
t 2766
 
3.3%
Other values (15) 17261
20.8%
Space Separator
ValueCountFrequency (%)
1710
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83042
98.0%
Common 1710
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11097
13.4%
i 10634
12.8%
n 9739
11.7%
o 7974
9.6%
r 5594
 
6.7%
e 5049
 
6.1%
l 4861
 
5.9%
s 4654
 
5.6%
c 3413
 
4.1%
t 2766
 
3.3%
Other values (15) 17261
20.8%
Common
ValueCountFrequency (%)
1710
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11097
13.1%
i 10634
12.5%
n 9739
11.5%
o 7974
9.4%
r 5594
 
6.6%
e 5049
 
6.0%
l 4861
 
5.7%
s 4654
 
5.5%
c 3413
 
4.0%
t 2766
 
3.3%
Other values (16) 18971
22.4%

Postal Code
Real number (ℝ)

High correlation 

Distinct631
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55206.14
Minimum1040
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2025-07-17T14:20:59.678008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1040
5-th percentile10009
Q123223
median57103
Q390008
95-th percentile98006
Maximum99301
Range98261
Interquartile range (IQR)66785

Descriptive statistics

Standard deviation32066.719
Coefficient of variation (CV)0.58085421
Kurtosis-1.4928665
Mean55206.14
Median Absolute Deviation (MAD)32929
Skewness-0.12951533
Sum5.5128851 × 108
Variance1.0282744 × 109
MonotonicityNot monotonic
2025-07-17T14:20:59.814249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10035 263
 
2.6%
10024 230
 
2.3%
10009 228
 
2.3%
94122 203
 
2.0%
10011 193
 
1.9%
94110 166
 
1.7%
98105 165
 
1.7%
19134 160
 
1.6%
90049 151
 
1.5%
98103 151
 
1.5%
Other values (621) 8076
80.9%
ValueCountFrequency (%)
1040 1
 
< 0.1%
1453 6
 
0.1%
1752 2
 
< 0.1%
1810 4
 
< 0.1%
1841 33
0.3%
1852 16
0.2%
1915 3
 
< 0.1%
2038 17
0.2%
2138 6
 
0.1%
2148 3
 
< 0.1%
ValueCountFrequency (%)
99301 6
 
0.1%
99207 7
 
0.1%
98661 5
 
0.1%
98632 3
 
< 0.1%
98502 5
 
0.1%
98270 2
 
< 0.1%
98226 3
 
< 0.1%
98208 1
 
< 0.1%
98198 7
 
0.1%
98115 112
1.1%

Region
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
west
3202 
east
2845 
central
2323 
south
1616 

Length

Max length7
Median length4
Mean length4.8597036
Min length4

Characters and Unicode

Total characters48529
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweast
2nd rowwest
3rd rowwest
4th rowsouth
5th roweast

Common Values

ValueCountFrequency (%)
west 3202
32.1%
east 2845
28.5%
central 2323
23.3%
south 1616
16.2%

Length

2025-07-17T14:20:59.969048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T14:21:00.079165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
west 3202
32.1%
east 2845
28.5%
central 2323
23.3%
south 1616
16.2%

Most occurring characters

ValueCountFrequency (%)
t 9986
20.6%
e 8370
17.2%
s 7663
15.8%
a 5168
10.6%
w 3202
 
6.6%
c 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
o 1616
 
3.3%
Other values (2) 3232
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48529
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 9986
20.6%
e 8370
17.2%
s 7663
15.8%
a 5168
10.6%
w 3202
 
6.6%
c 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
o 1616
 
3.3%
Other values (2) 3232
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 48529
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 9986
20.6%
e 8370
17.2%
s 7663
15.8%
a 5168
10.6%
w 3202
 
6.6%
c 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
o 1616
 
3.3%
Other values (2) 3232
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 9986
20.6%
e 8370
17.2%
s 7663
15.8%
a 5168
10.6%
w 3202
 
6.6%
c 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
o 1616
 
3.3%
Other values (2) 3232
 
6.7%

Category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
office supplies
6022 
furniture
2119 
technology
1845 

Length

Max length15
Median length15
Mean length12.803024
Min length9

Characters and Unicode

Total characters127851
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtechnology
2nd rowfurniture
3rd rowoffice supplies
4th rowoffice supplies
5th rowoffice supplies

Common Values

ValueCountFrequency (%)
office supplies 6022
60.3%
furniture 2119
 
21.2%
technology 1845
 
18.5%

Length

2025-07-17T14:21:00.201895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T14:21:00.304610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
office 6022
37.6%
supplies 6022
37.6%
furniture 2119
 
13.2%
technology 1845
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e 16008
12.5%
f 14163
11.1%
i 14163
11.1%
s 12044
9.4%
p 12044
9.4%
u 10260
8.0%
o 9712
7.6%
c 7867
6.2%
l 7867
6.2%
6022
 
4.7%
Other values (6) 17701
13.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121829
95.3%
Space Separator 6022
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16008
13.1%
f 14163
11.6%
i 14163
11.6%
s 12044
9.9%
p 12044
9.9%
u 10260
8.4%
o 9712
8.0%
c 7867
6.5%
l 7867
6.5%
r 4238
 
3.5%
Other values (5) 13463
11.1%
Space Separator
ValueCountFrequency (%)
6022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 121829
95.3%
Common 6022
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16008
13.1%
f 14163
11.6%
i 14163
11.6%
s 12044
9.9%
p 12044
9.9%
u 10260
8.4%
o 9712
8.0%
c 7867
6.5%
l 7867
6.5%
r 4238
 
3.5%
Other values (5) 13463
11.1%
Common
ValueCountFrequency (%)
6022
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16008
12.5%
f 14163
11.1%
i 14163
11.1%
s 12044
9.4%
p 12044
9.4%
u 10260
8.0%
o 9712
7.6%
c 7867
6.2%
l 7867
6.2%
6022
 
4.7%
Other values (6) 17701
13.8%

Sub-Category
Categorical

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
binders
1522 
paper
1368 
furnishings
956 
phones
889 
storage
845 
Other values (12)
4406 

Length

Max length11
Median length9
Mean length7.1911676
Min length3

Characters and Unicode

Total characters71811
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowphones
2nd rowtables
3rd rowbinders
4th rowpaper
5th rowbinders

Common Values

ValueCountFrequency (%)
binders 1522
15.2%
paper 1368
13.7%
furnishings 956
9.6%
phones 889
8.9%
storage 845
8.5%
art 796
8.0%
accessories 773
7.7%
chairs 616
6.2%
appliances 466
 
4.7%
labels 364
 
3.6%
Other values (7) 1391
13.9%

Length

2025-07-17T14:21:00.431417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders 1522
15.2%
paper 1368
13.7%
furnishings 956
9.6%
phones 889
8.9%
storage 845
8.5%
art 796
8.0%
accessories 773
7.7%
chairs 616
6.2%
appliances 466
 
4.7%
labels 364
 
3.6%
Other values (7) 1391
13.9%

Most occurring characters

ValueCountFrequency (%)
s 10959
15.3%
e 9116
12.7%
r 7161
10.0%
a 6573
9.2%
i 5662
7.9%
n 5375
7.5%
p 5259
7.3%
o 3285
 
4.6%
c 3039
 
4.2%
h 2576
 
3.6%
Other values (10) 12806
17.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71811
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 10959
15.3%
e 9116
12.7%
r 7161
10.0%
a 6573
9.2%
i 5662
7.9%
n 5375
7.5%
p 5259
7.3%
o 3285
 
4.6%
c 3039
 
4.2%
h 2576
 
3.6%
Other values (10) 12806
17.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 71811
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 10959
15.3%
e 9116
12.7%
r 7161
10.0%
a 6573
9.2%
i 5662
7.9%
n 5375
7.5%
p 5259
7.3%
o 3285
 
4.6%
c 3039
 
4.2%
h 2576
 
3.6%
Other values (10) 12806
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 10959
15.3%
e 9116
12.7%
r 7161
10.0%
a 6573
9.2%
i 5662
7.9%
n 5375
7.5%
p 5259
7.3%
o 3285
 
4.6%
c 3039
 
4.2%
h 2576
 
3.6%
Other values (10) 12806
17.8%
Distinct1846
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
2025-07-17T14:21:00.873943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length120
Median length74
Mean length35.88584
Min length5

Characters and Unicode

Total characters358356
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st rowatt el51110 dect
2nd rowhon 2111 invitation series corner table
3rd rowwilson jones ledgersize pianohinge binder 2 blue
4th rowxerox 1887
5th rowpressboard covers with storage hooks 9 12 x 11 light blue
ValueCountFrequency (%)
xerox 863
 
1.6%
x 701
 
1.3%
with 598
 
1.1%
avery 557
 
1.0%
for 538
 
1.0%
binders 524
 
0.9%
chair 478
 
0.9%
black 424
 
0.8%
phone 374
 
0.7%
gbc 341
 
0.6%
Other values (2753) 49989
90.3%
2025-07-17T14:21:01.471758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45616
 
12.7%
e 35563
 
9.9%
r 22802
 
6.4%
a 21990
 
6.1%
o 21286
 
5.9%
s 20954
 
5.8%
i 19940
 
5.6%
l 18633
 
5.2%
t 17147
 
4.8%
n 16406
 
4.6%
Other values (33) 118019
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 294261
82.1%
Space Separator 46041
 
12.8%
Decimal Number 17963
 
5.0%
Control 86
 
< 0.1%
Other Number 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 35563
12.1%
r 22802
 
7.7%
a 21990
 
7.5%
o 21286
 
7.2%
s 20954
 
7.1%
i 19940
 
6.8%
l 18633
 
6.3%
t 17147
 
5.8%
n 16406
 
5.6%
c 14915
 
5.1%
Other values (18) 84625
28.8%
Decimal Number
ValueCountFrequency (%)
1 3777
21.0%
0 2920
16.3%
2 2268
12.6%
4 1723
9.6%
3 1530
8.5%
5 1443
 
8.0%
8 1252
 
7.0%
9 1232
 
6.9%
6 938
 
5.2%
7 880
 
4.9%
Space Separator
ValueCountFrequency (%)
45616
99.1%
  425
 
0.9%
Control
ValueCountFrequency (%)
” 67
77.9%
“ 19
 
22.1%
Other Number
ValueCountFrequency (%)
¾ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 294261
82.1%
Common 64095
 
17.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 35563
12.1%
r 22802
 
7.7%
a 21990
 
7.5%
o 21286
 
7.2%
s 20954
 
7.1%
i 19940
 
6.8%
l 18633
 
6.3%
t 17147
 
5.8%
n 16406
 
5.6%
c 14915
 
5.1%
Other values (18) 84625
28.8%
Common
ValueCountFrequency (%)
45616
71.2%
1 3777
 
5.9%
0 2920
 
4.6%
2 2268
 
3.5%
4 1723
 
2.7%
3 1530
 
2.4%
5 1443
 
2.3%
8 1252
 
2.0%
9 1232
 
1.9%
6 938
 
1.5%
Other values (5) 1396
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 357823
99.9%
None 533
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45616
 
12.7%
e 35563
 
9.9%
r 22802
 
6.4%
a 21990
 
6.1%
o 21286
 
5.9%
s 20954
 
5.9%
i 19940
 
5.6%
l 18633
 
5.2%
t 17147
 
4.8%
n 16406
 
4.6%
Other values (27) 117486
32.8%
None
ValueCountFrequency (%)
  425
79.7%
” 67
 
12.6%
“ 19
 
3.6%
é 14
 
2.6%
¾ 5
 
0.9%
à 3
 
0.6%

Sales
Real number (ℝ)

High correlation 

Distinct5826
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.04215
Minimum0.444
Maximum22638.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2025-07-17T14:21:01.596761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.444
5-th percentile4.98
Q117.248
median54.432
Q3209.9375
95-th percentile957.34933
Maximum22638.48
Range22638.036
Interquartile range (IQR)192.6895

Descriptive statistics

Standard deviation623.66752
Coefficient of variation (CV)2.7111011
Kurtosis304.71558
Mean230.04215
Median Absolute Deviation (MAD)45.42
Skewness12.957949
Sum2297200.9
Variance388961.17
MonotonicityNot monotonic
2025-07-17T14:21:01.734050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.96 56
 
0.6%
19.44 39
 
0.4%
15.552 39
 
0.4%
10.368 36
 
0.4%
25.92 36
 
0.4%
32.4 28
 
0.3%
6.48 21
 
0.2%
17.94 21
 
0.2%
20.736 19
 
0.2%
14.94 17
 
0.2%
Other values (5816) 9674
96.9%
ValueCountFrequency (%)
0.444 1
 
< 0.1%
0.556 1
 
< 0.1%
0.836 1
 
< 0.1%
0.852 1
 
< 0.1%
0.876 1
 
< 0.1%
0.898 1
 
< 0.1%
0.984 1
 
< 0.1%
0.99 1
 
< 0.1%
1.044 1
 
< 0.1%
1.08 3
< 0.1%
ValueCountFrequency (%)
22638.48 1
< 0.1%
17499.95 1
< 0.1%
13999.96 1
< 0.1%
11199.968 1
< 0.1%
10499.97 1
< 0.1%
9892.74 1
< 0.1%
9449.95 1
< 0.1%
9099.93 1
< 0.1%
8749.95 1
< 0.1%
8399.976 1
< 0.1%

Quantity
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7926097
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2025-07-17T14:21:01.840718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2333901
Coefficient of variation (CV)0.58887952
Kurtosis2.0998452
Mean3.7926097
Median Absolute Deviation (MAD)1
Skewness1.2980663
Sum37873
Variance4.9880315
MonotonicityNot monotonic
2025-07-17T14:21:01.951784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 2407
24.1%
2 2398
24.0%
5 1229
12.3%
4 1191
11.9%
1 899
 
9.0%
7 605
 
6.1%
6 570
 
5.7%
9 257
 
2.6%
8 256
 
2.6%
10 57
 
0.6%
Other values (6) 117
 
1.2%
ValueCountFrequency (%)
1 899
 
9.0%
2 2398
24.0%
3 2407
24.1%
4 1191
11.9%
5 1229
12.3%
6 570
 
5.7%
7 605
 
6.1%
8 256
 
2.6%
9 257
 
2.6%
10 57
 
0.6%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 1
 
< 0.1%
14 29
 
0.3%
13 27
 
0.3%
12 25
 
0.3%
11 34
 
0.3%
10 57
 
0.6%
9 257
2.6%
8 256
2.6%
7 605
6.1%

Discount
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15625776
Minimum0
Maximum0.8
Zeros4793
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2025-07-17T14:21:02.063933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.2
95-th percentile0.7
Maximum0.8
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.20649912
Coefficient of variation (CV)1.3215288
Kurtosis2.4069541
Mean0.15625776
Median Absolute Deviation (MAD)0.2
Skewness1.6838712
Sum1560.39
Variance0.042641888
MonotonicityNot monotonic
2025-07-17T14:21:02.182117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 4793
48.0%
0.2 3655
36.6%
0.7 418
 
4.2%
0.8 300
 
3.0%
0.3 226
 
2.3%
0.4 206
 
2.1%
0.6 138
 
1.4%
0.1 94
 
0.9%
0.5 66
 
0.7%
0.15 52
 
0.5%
Other values (2) 38
 
0.4%
ValueCountFrequency (%)
0 4793
48.0%
0.1 94
 
0.9%
0.15 52
 
0.5%
0.2 3655
36.6%
0.3 226
 
2.3%
0.32 27
 
0.3%
0.4 206
 
2.1%
0.45 11
 
0.1%
0.5 66
 
0.7%
0.6 138
 
1.4%
ValueCountFrequency (%)
0.8 300
 
3.0%
0.7 418
 
4.2%
0.6 138
 
1.4%
0.5 66
 
0.7%
0.45 11
 
0.1%
0.4 206
 
2.1%
0.32 27
 
0.3%
0.3 226
 
2.3%
0.2 3655
36.6%
0.15 52
 
0.5%

Profit
Real number (ℝ)

High correlation 

Distinct7284
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.679854
Minimum-6599.978
Maximum8399.976
Zeros65
Zeros (%)0.7%
Negative1870
Negative (%)18.7%
Memory size78.1 KiB
2025-07-17T14:21:02.311833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-6599.978
5-th percentile-53.0562
Q11.728
median8.64135
Q329.3625
95-th percentile169.0041
Maximum8399.976
Range14999.954
Interquartile range (IQR)27.6345

Descriptive statistics

Standard deviation234.39483
Coefficient of variation (CV)8.172804
Kurtosis396.58955
Mean28.679854
Median Absolute Deviation (MAD)10.78535
Skewness7.5552389
Sum286397.02
Variance54940.934
MonotonicityNot monotonic
2025-07-17T14:21:02.444056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
0.7%
6.2208 43
 
0.4%
9.3312 38
 
0.4%
3.6288 32
 
0.3%
5.4432 32
 
0.3%
15.552 26
 
0.3%
12.4416 21
 
0.2%
7.2576 19
 
0.2%
3.1104 18
 
0.2%
9.072 11
 
0.1%
Other values (7274) 9681
96.9%
ValueCountFrequency (%)
-6599.978 1
< 0.1%
-3839.9904 1
< 0.1%
-3701.8928 1
< 0.1%
-3399.98 1
< 0.1%
-2929.4845 1
< 0.1%
-2639.9912 1
< 0.1%
-2287.782 1
< 0.1%
-1862.3124 1
< 0.1%
-1850.9464 1
< 0.1%
-1811.0784 1
< 0.1%
ValueCountFrequency (%)
8399.976 1
< 0.1%
6719.9808 1
< 0.1%
5039.9856 1
< 0.1%
4946.37 1
< 0.1%
4630.4755 1
< 0.1%
3919.9888 1
< 0.1%
3177.475 1
< 0.1%
2799.984 1
< 0.1%
2591.9568 1
< 0.1%
2504.2216 1
< 0.1%

Profit Margin
Real number (ℝ)

High correlation 

Distinct572
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12018479
Minimum-2.75
Maximum0.5
Zeros65
Zeros (%)0.7%
Negative1870
Negative (%)18.7%
Memory size78.1 KiB
2025-07-17T14:21:02.572426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.75
5-th percentile-0.76666667
Q10.075
median0.27
Q30.3625
95-th percentile0.48
Maximum0.5
Range3.25
Interquartile range (IQR)0.2875

Descriptive statistics

Standard deviation0.46689386
Coefficient of variation (CV)3.8848
Kurtosis10.164857
Mean0.12018479
Median Absolute Deviation (MAD)0.17
Skewness-2.8938446
Sum1200.1653
Variance0.21798988
MonotonicityNot monotonic
2025-07-17T14:21:02.714071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26 258
 
2.6%
0.35 246
 
2.5%
0.48 204
 
2.0%
0.47 201
 
2.0%
0.48 189
 
1.9%
0.35 185
 
1.9%
0.3625 177
 
1.8%
0.325 171
 
1.7%
0.29 169
 
1.7%
0.125 163
 
1.6%
Other values (562) 8023
80.3%
ValueCountFrequency (%)
-2.75 4
< 0.1%
-2.7 8
0.1%
-2.7 6
0.1%
-2.65 5
0.1%
-2.6 6
0.1%
-2.6 4
< 0.1%
-2.55 4
< 0.1%
-2.55 9
0.1%
-2.5 9
0.1%
-2.5 1
 
< 0.1%
ValueCountFrequency (%)
0.5 140
1.4%
0.49 5
 
0.1%
0.49 74
 
0.7%
0.49 157
1.6%
0.49 79
 
0.8%
0.48 10
 
0.1%
0.48 189
1.9%
0.48 204
2.0%
0.48 106
1.1%
0.47 18
 
0.2%

Interactions

2025-07-17T14:20:52.186485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:49.121287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:49.749430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.327675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.936461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.565721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:52.285146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:49.240103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:49.843046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.427035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.036379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.667726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:52.377338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:49.331932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:49.929170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.518958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.133269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.760163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:52.475306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:49.437861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.027875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.620096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.241766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.868009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:52.590294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:49.547072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.130591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.728928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.347851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.979950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:52.696522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:49.648868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.231533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:50.835409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:51.460479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-17T14:20:52.076978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-07-17T14:21:02.830075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CategoryDiscountPostal CodeProfitProfit MarginQuantityRegionSalesSegmentShip ModeStateSub-Category
Category1.0000.3770.0000.0570.2710.0000.0000.0720.0000.0000.0180.999
Discount0.3771.0000.052-0.543-0.645-0.0010.294-0.0570.0050.0270.3540.353
Postal Code0.0000.0521.000-0.005-0.0280.0130.921-0.0020.0350.0380.9680.000
Profit0.057-0.543-0.0051.0000.5000.2340.0210.5180.0000.0050.0170.130
Profit Margin0.271-0.645-0.0280.5001.0000.0010.204-0.2000.0160.0120.2280.304
Quantity0.000-0.0010.0130.2340.0011.0000.0150.3280.0160.0000.0260.000
Region0.0000.2940.9210.0210.2040.0151.0000.0000.0000.0220.9980.000
Sales0.072-0.057-0.0020.518-0.2000.3280.0001.0000.0020.0000.0000.142
Segment0.0000.0050.0350.0000.0160.0160.0000.0021.0000.0330.0900.000
Ship Mode0.0000.0270.0380.0050.0120.0000.0220.0000.0331.0000.0960.007
State0.0180.3540.9680.0170.2280.0260.9980.0000.0900.0961.0000.000
Sub-Category0.9990.3530.0000.1300.3040.0000.0000.1420.0000.0070.0001.000

Missing values

2025-07-17T14:20:52.867830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-17T14:20:53.180054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Order IDProduct IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionCategorySub-CategoryProduct NameSalesQuantityDiscountProfitProfit Margin
0CA-2014-100006TEC-PH-100020759/7/20149/13/2014standard classdk13375dennis kaneconsumerunited statesnew york citynew york10024easttechnologyphonesatt el51110 dect377.97030.0109.61130.290000
1CA-2014-100090FUR-TA-100037157/8/20147/12/2014standard classeb13705ed braxtoncorporateunited statessan franciscocalifornia94122westfurnituretableshon 2111 invitation series corner table502.48830.2-87.9354-0.175000
2CA-2014-100090OFF-BI-100015977/8/20147/12/2014standard classeb13705ed braxtoncorporateunited statessan franciscocalifornia94122westoffice suppliesbinderswilson jones ledgersize pianohinge binder 2 blue196.70460.268.84640.350000
3CA-2014-100293OFF-PA-100001763/14/20143/18/2014standard classnf18475neil französischhome officeunited statesjacksonvilleflorida32216southoffice suppliespaperxerox 188791.05660.231.86960.350000
4CA-2014-100328OFF-BI-100003431/28/20142/3/2014standard classjc15340jasper cacioppoconsumerunited statesnew york citynew york10024eastoffice suppliesbinderspressboard covers with storage hooks 9 12 x 11 light blue3.92810.21.32570.337500
5CA-2014-100363OFF-FA-100006114/8/20144/15/2014standard classjm15655jim mitchumcorporateunited statesglendalearizona85301westoffice suppliesfastenersbinder clips by oic2.36820.20.82880.350000
6CA-2014-100363OFF-PA-100047334/8/20144/15/2014standard classjm15655jim mitchumcorporateunited statesglendalearizona85301westoffice suppliespaperthings to do today spiral book19.00830.26.89040.362500
7CA-2014-100391OFF-PA-100014715/25/20145/29/2014standard classbw11065barry weirichconsumerunited statesnew york citynew york10035eastoffice suppliespaperstrathmore photo frame cards14.62020.06.72520.460000
8CA-2014-100678FUR-CH-100026024/18/20144/22/2014standard classkm16720kunst millerconsumerunited stateshoustontexas77095centralfurniturechairsdmi arturo collection missionstyle design wood chair317.05830.3-18.1176-0.057143
9CA-2014-100678OFF-AR-100018684/18/20144/22/2014standard classkm16720kunst millerconsumerunited stateshoustontexas77095centraloffice suppliesartprang dustless chalk sticks2.68820.21.00800.375000
Order IDProduct IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionCategorySub-CategoryProduct NameSalesQuantityDiscountProfitProfit Margin
9976US-2017-169488OFF-PA-100001579/7/20179/9/2017first classaa10375allen armoldconsumerunited statesprovidencerhode island2908eastoffice suppliespaperxerox 19139.96020.018.78120.470000
9977US-2017-169488OFF-PA-100026599/7/20179/9/2017first classaa10375allen armoldconsumerunited statesprovidencerhode island2908eastoffice suppliespaperavoid verbal orders carbonless minifold book16.90050.07.77400.460000
9978US-2017-169502OFF-AP-100019478/28/20179/1/2017standard classmg17650matthew grinsteinhome officeunited statesmilwaukeewisconsin53209centraloffice suppliesappliancesacco 6 outlet guardian premium plus surge suppressor91.60050.026.56400.290000
9979US-2017-169502OFF-SU-100041158/28/20179/1/2017standard classmg17650matthew grinsteinhome officeunited statesmilwaukeewisconsin53209centraloffice suppliessuppliesacme stainless steel office snips21.81030.05.88870.270000
9980US-2017-169551FUR-BO-100015197/7/20177/9/2017first classrl19615rob lucasconsumerunited statesphiladelphiapennsylvania19120eastfurniturebookcasesosullivan 3shelf heavyduty bookcases87.21030.5-45.3492-0.520000
9981US-2017-169551OFF-PA-100041007/7/20177/9/2017first classrl19615rob lucasconsumerunited statesphiladelphiapennsylvania19120eastoffice suppliespaperxerox 21615.55230.25.44320.350000
9982US-2017-169551OFF-ST-100048357/7/20177/9/2017first classrl19615rob lucasconsumerunited statesphiladelphiapennsylvania19120eastoffice suppliesstorageplastic stacking crates casters13.39230.21.00440.075000
9983US-2017-169551TEC-AC-100020187/7/20177/9/2017first classrl19615rob lucasconsumerunited statesphiladelphiapennsylvania19120easttechnologyaccessoriesamazonbasics 3button usb wired mouse16.77630.24.82310.287500
9984US-2017-169551TEC-AC-100030337/7/20177/9/2017first classrl19615rob lucasconsumerunited statesphiladelphiapennsylvania19120easttechnologyaccessoriesplantronics cs510 overthehead monaural wireless headset system527.92020.285.78700.162500
9985US-2017-169551TEC-PH-100013637/7/20177/9/2017first classrl19615rob lucasconsumerunited statesphiladelphiapennsylvania19120easttechnologyphonesapple iphone 5s683.98820.4-113.9980-0.166667